Filter Bubbles

Just a few days ago, I receive notice from Instagram that they were accelerating the shutdown of the bulk of their API features, including posting on user’s behalf, searching for results and subscribing to updates from particular users. Ostensibly this is to cut down on using these tools for nefarious reasons as Instagram’s parent company, Facebook, continues to reel from the impact of Cambridge Analytica using FB data to target US citizens with propaganda and potentially impact the election. There have been cries from users to move off Facebook, delete their profiles, and move their peeps to another network. Most famously, Will Ferrell has deleted his account. A corporate alumni group I am a member if is considering moving to Slack. For most everyone else, it is business as usual on the world’s largest social network. Mainly because the consumer privacy frogs have been boiled to death. Most consumers do not realize that when they use Facebook or other social network accounts to log in to new websites, they are sharing their entire social graph with that site. That is how Cambridge Analytica, buying data through a Cambridge researcher and other channels, was able to capture the details of 87 million Americans. Most users do not know what they are signing up for, how their data is being used, and click through privacy updates faster than Las Vegas retirees at penny slots.

Only a few consumers have jumped out of the hot water social networks have gotten them into. Our collective frogs are being boiled.

What we see on the backend, where the social sausage is made, is that firms like Instagram and Twitter are changing what data people can get access to as the horror of what is possible is moving from the dark web to public headlines. Twitter, a few weeks ago, started shutting down so called “TweetDeckers,” accounts that build and maintain a captured set of followers to artificially amplify their message across that social network. Facebook moved their public search api behind their firewall and provided curated access through data provider Datasift a few years ago, mixing public and private in a way so that marketers could target ads more broadly but have less access to the details of who they are targeting.

What we have not yet seen from these firms is more transparency to their users as to what is going on. Twitter did not market the fake influencers on their network as Tweetdeckers, they just suspended their accounts, most only temporarily. Many of these offenders just opened new accounts, both to continue their operations as usual and to complain to their tribe of followers (mostly bots) that they were wronged by Twitter. Our own analysis at Argus Insights found that most of the content in B2B discussions on Twitter are published by compromised accounts. In the Internet of Things market alone, 75% of the content published is from accounts compromised in some way, yet we see nothing from Twitter to help sort the wheat from the chaff. Spammers use Twitter’s API to drop in latitude and longitude information to boost their discovery in local search results. (It is hilarious to see Tweets published from the geographic center of Palo Alto, evidently published from a phone located inside the concrete walls of one of the buildings there).

It’s an arms race, I know, trying to keep up with how people will leverage tools for both good and evil. By the very nature of Silicon Valley, we want to build more tools, more API’s to share even more data, because that level of transparency is good, right? If plutonium can be used to craft weapons of mass destruction, social data can be used to build and deploy weapons of mass disruption. And here’s the thing, just like arms dealers making bank selling weapons to both sides of battles in small African countries, the social networks make their quarters (fiscal and literal) by the volume of advertising running on their platforms. I do not begrudge these firms making benjamins on my eyeballs. I understand they are turning my social capital into financial capital. But as a buyer of advertising, I want to make sure I get what I pay for.

Imagine if CBS Interactive charged billboard advertisers for space for the 101 freeway in Silicon Valley based on the number of chickens making their way from LA to Napa? Chickens have eyeballs, they are the target market since they are on the freeway in Silicon Valley. Why should Chipotle or Saleforce.com cry foul if CBS Interactive charges a premium for reaching an tract 30 million eyeballs a month? This is the equivalent of the debacle we call social advertising today. I had a client whose ad campaign netted over 43,000 new followers for their account. Of those new followers, less than 200 had participated in their market in the last six months. They paid good money to reach prospects, not chickens, which is what they got instead. The social firms can maintain and grow their business if they do a better job of ensuring the quality of their networks, and provide metrics beyond fake followers and fake reach.

And tell their users how their data is being used. Make it transparent how the data is being used to target users to the mutual benefit to advertises, the platform and users. We watch TV, we know need to see ads. Heck, we watch the Superbowl because of the ads! We read the paper, we see ads, we search in Google, we see ads. We know we pay for things with our attention.

In summary….

Social networks are working behind the scenes to make it harder to misuse their data and tool but will always fall behind the arms race of nefarious actors peddling influence

Consumers have had their privacy concerns boiled out of them but still need to be made away (in simple language) the impact of their actions

Advertisers should demand higher quality targeting of people, not chickens along with better metrics of success or failure of campaigns

Someone has or will recommend that Blockchain and/or Artificial Intelligence will solve all of these problems if you just gave them enough funding…

Ahead of the news from Facebook on Cambridge Analytica abusing the data of American citizens, Twitter started suspending accounts for a practice called Tweetdecking, where accounts would solicit and at times pay for accounts to artificially boost their classic influence metrics of reach and followers. Over the weekend of 18 March, some of the Internet of Things most prolific influencers were also suspended, at least for a while. Many of them are back on the Twitter-waves, pushing content like before but the artificial amplification they received by Tweetdecking has diminished to mortal levels.

At Argus Insights, we have been doing our own analysis of the rampant pay to post shenanigans in the various B2B markets we track. I first identified the issue when we saw Brocade jump in 2015 after a product announcement. Our client at the time, HPE, was concerned about the amount of attention that Brocade was getting for their announcement of offering free Network Functional Virtualization solutions. We dug into the data, initially by hand, and found the bulk of the lift Brocade demonstrated came from two sources, their own employees (Corporate Narcississm) and what appeared to be bot accounts from Saudi Arabia, tweets from people that had no interest in the telecom space, based on their past social engagements. In short, someone, maybe Brocade, maybe their agency, or someone else, was paying to push their message out, inflate their metrics and give the perception that the whole market cared about their announcement. Turns out the market didn’t care. Without the bots and employees boosting the interest, the chatter around Brocade died down to normal levels almost the next day.

We had a client looking to boost their followers (the boss challenged them to beat his follower count in a few weeks) and ignored our advice to build their following organically. They instead spent money on Twitter ads and saw their followers go from 92 to over 40,ooo in just a few weeks. The boss cried foul and asked them to prove these were real followers. They came back to Argus Insights, hat in hand, and asked if we could help. By looking at which of their followers had actively participated in the market (NFV) in the last six months, we could say which of their 40k followers were likely to be real. It was only 271. Of over 40k followers they had grabbed with their ad campaign, less than 1% mattered to their business. The client started managing to their True Followers metric instead and saw their authentic influence grow, even as their overall follower count dropped.

But not everyone that participates in a market is part of that market. In November of 2017, after being frustrated with the amount of poor content that was topping the charts of our analysis within IoT, I developed some metrics to gauge whether an account fit into a few different categories. Were they a brand, pushing out content mostly from a single domain with a good level of active dialogue with the rest of market? Were they a broadcaster, just sharing content from others? We also identified content farms, accounts that tend to talk about themselves a lot and retweet content of their clients. The most nefarious type were the compromised accounts. These accounts are basically owned by content farms and seek only to artificially boost their ‘influence’ in the marketplace.

Once we had applied this account types, we found that over 75% of all IoT content was published by the compromised accounts. This means that 3 out of 4 tweets about a multi-billion dollar market are not authentic and serve only to misguide and misdirect the 25% of the content that is more likely legitimate.

It gets worse. One of the most prolific IoT influencers pushed out almost 700 tweets, over 10% were self promotion, a clear sign of a content farm. More nefarious is that of his thousands of retweets, 85%, eighty-five percent, came from the aforementioned compromised accounts. This means that only 15% of his “influence” is legitimate. This means his clients that rely on his reach to bolster their own market awareness are paying to push content to compromised accounts. Not customers, not influencers, not thought leaders, but accounts owned by others whose sole purpose is to buy and sell attention from others.

B2B marketing on Twitter is broken. Broken by those that would game the system and misrepresent their own influence. Broken by those firms that pay for influence rather than earning it. Broken for those who see Twitter as a source of what is happening in their market. Broken for those looking to see what trends are driving their markets. Broken for you…

Discussions of Artificial Intelligence (AI) are everywhere, ironically enough, some of it written and published by bots… Elon Musk continues to gain headlines for his crusade against the development of AI, citing concerns that if we pick the wrong utility function, humans could be optimized out of existence. (from his talk with Walter Issacson, if the goal is to eliminate spam, the AI could decide that eliminating humans is the most efficient path). IBM’s Watson has been the poster child of deep learning, promised to be the panacea for everything from call center routing to finding copyright infringement to winning at Jeopardy. But what does this mean for the every day consumer? What happens when our homes really become ‘smart?’ Does it punish us for not cleaning the toilet? Does it decide, based on reading the news feeds, it would be safer for the family not to leave the house and lock us in, delivering food via Grub Hub and Amazon drones?

The promise and threat of the Smart Home requires thinking through the types of intelligences required.

We need a way to think about AI in the home so we can better design these systems to anticipate the benefits and well as where a ‘rogue’ intelligence could go off the rails. Much the way our own brain segments functions in different sections of our gray matter, home intelligence could also benefit from some sense of specialization and hierarchy. AI systems are really good at recognizing patterns and determining action based on those patterns. Within the home, you can consider five types of intelligences needed to realize the true promise of the Smart Home.

Visual Intelligence

Behavioral Recognition

Human Interface Engine

Threat detection and abatement

Ethics Engine

The Visual Intelligence is the most common being used today, looking at streams of visual data, static and dynamic, coming from security cameras, phone snapshots and more. These maturing intelligences can distinguish between dogs and cats in the scene, ensure only family members are allowed in the back door, and with Apple’s new A11 Bionic on the iPhone X, process your face fast enough to paint your expressions onto an animated emoji of poo. The trend is to push as much of this recognition to the edge so that the homes do not have to push terabytes of data up to the cloud every time someone rings your doorbell camera. In the tug-a-o-war between privacy and security, edge process of the visual data from your home also ensures your data stays yours as well as lowers the latency in recognition. The interpretation of visual information in the home is a key requirement for any home intelligence as correct evaluation of what is happening visual drives much of the follow-on actions in the home. Adjusting the thermostat based on the number of people in the room. Letting people know when the dog went outside for a potty break and more.

Behavioral Recognition builds on the information within the visual scene of the home and interprets the behavior of the occupants to look for patterns of engagement that can be automated. Vivint’s new Sky intelligence examines what actions the occupants take inside of their homes and takes on those tasks over time. For example, if when you leave in the morning, you turn down the thermostat, turn on the back porch light, switch off your stream of classical music from Alexa and send a text to the nanny reminding them of pickup, Sky promises to, over time, do most of these as you walk out the door. The same happens when you come home, the right lights turn on, smooth jazz is piped through the kitchen, and the house has already been working on achieving the right comfort level as soon as it knew you were headed home. This Behavioral Recognition extrapolates within your routine (and less routine) actions to determine candidates for automation while at the same time understanding when you deviate from your normal routines and how the react appropriately. Say you come home with extra kids for a play date, smooth jazz probably isn’t the vibe you are going for. Or shift your thermostat because Mom is visiting. The Behavioral Recognition is critical to having your home feel intelligence and tailored to your context.

Of course the occupants need to interact with the home. The Human Interface Engine bridges that gap between human and home. We’ve seen the rapid rise of Alexa and Google Home (and eventually Bixby and HomePod) leverage voice as key interface to our home environment. But there are also gestures, apps, touch and more that drive our engagement. The Human Interface Engine will be that collection of intelligences that gather and interpret those actions on behalf of the human occupants. Want the kids to exercise more? Require 200 jumping jacks before they can turn on the Xbox? Wave goodnight to your living room to turn off the music and the lights before retiring for the night. Filter out your teenager’s commands every time she wants Alexa to play death metal. Without the ability to recognize and react to the ultimate users in the Smart Home, the humans, any system would only be guessing at what do to on behalf of their occupants.

Security becomes critical as well within the home environment both for the ability for the bad guys to get access to our resources but also for the ability for nefarious actors to leverage our homes to attack others. As such, a local intelligence that is focused on Threat Detection and Abatement is critical to any Smart Home intelligence solution. Solutions like Cujo bring solutions typically reserved for the enterprise into the home and are a good start towards the type of security needed in the home. Cujo and others leverage the attack patterns sensed across their customers to continuously update their own understanding of evolving threats. Eventually, the abatement of threats to the home could also include counter-intelligence capabilities, spoofing data and usage information as a way to throw off the intentions of the bad guys. Are you on vacation or just taking quick trip to Costco? Home with a sick child or on a business trip? This type of misinformation also becomes a new direction of capabilities for these intelligences and aid in the overall security of the home and its users.

The final intelligence we believe is necessary in the home is an Ethics Engine. The Ethics Engine establishes the bounds of what is the correct actions for the home to take. Just because you can turn the thermostat down to 40 degrees does not mean that you should. Just because the dash button can order three lifetimes of Cheetos to be deliverd by Friday, does not mean is should. This notion has received a lot within Autonomous Vehicles, where the vehicle makes decisions regarding whether to hit the squirrel or the telephone pole. Within the home, this is also critical as more of the home’s systems become controllable by these intelligences. This ‘brain of brains’ will have the final sign-off on any actions taken by the Smart Home as the final line of defense of the users health, safety and wellbeing. Models of robo-ethics have been around since Issac Asimov first published his laws of robotics but they have not yet been applied to the Smart Home content. The Disney Channel movie, Smart House, saw a resurgence in popularity recently because of its campy portrayal of a home gone rogue. An Ethics Engine would prevent and more dangerous scenarios from happening, ensuring the home does not fulfill Elon Musk’s nightmare scenario of a rampant AI that eradicates the family hamster.

Within each of these five intelligences, you can see how this framework helps give context to the types of AI that needs to be brought together in concert in the Smart Home. True intelligence in our abodes will come from the seamless integration and collaboration within these critical capabilities. While no one company has brought all of these elements together, we see the capabilities evolving. Look for the service providers like Vivint and Comcast leading the charge, integrating the solutions where feasible and building their own where necessary. Eventually, we will have that home of the future, today, and feel safe using it.

Brain Machine Interfaces (BMI) have been part of the science fiction stable for years, but research has brought us closer to that reality every day. We are closer to enabling the control of devices without lifting a finger, a boon for those with injuries or disabilities that prevent them from controlling most devices. This could also work the other way. Sure, a BMI helped Neo learn Kung-Fu in seconds but it also left his mind and those of others jacked into the Matrix vulnerable to connections going from the machine world into their own carbon based processor.

Remember, the connection to the Matrix works both ways. Will Brain Machine Interfaces let the social networks do more than hack our news feeds?

We can already influence the other way just by controlling what content we consume. Everyone has their mood music, a playlist for coding, focusing, getting down and more. Facebook proved, through some internal research, that they could control the moods of members by adjusting their news feed. Now imagine if you didn’t have to go through people’s eyes but directly to their brain? This Internet of Thoughts could be a new type of digital voyeurism where celebrities and everyday people could share their emotional playlists. Want to get amped up for the bit swim meet, jack into Michael Phelps. Need a creative boost to hit the deadline, access the soul and feelings of Ogilvy for a few minutes. Several firms, like Thync, have already proven they can induce calming states externally with the right electrical stimulation.

In this world where our filter bubbles have already erected intellectual firewalls to protect us from views, theories, and evidence that might challenge our cherished world views, imagine if there was a way to hack around those firewalls and target our emotional centers directly? What if they could change your anchors so that every time you saw chocolate, it would trigger disgust. That for every glance of fake news, ecstasy would result. The potential for behavioral therapy is fantastic but there is that dark side to be concerned with. An evolving Internet of Thoughts coupled with the ability to control them more directly could edge us down that dystopian path first laid out by George Orwell in 1984.

At Argus Insights, we have a bit of an anecdote to these Filter Bubbles. Because we track every single piece of content related to a market, we help users pop their brand biased filter bubbles to see what is happening across the entire Internet of Things market. Our Analyzer platform helps you avoid the tunnel vision of brand biased engagement improves your market kung fu in a matter of seconds. If you are ready to take the red pill, check out the Analyzer today.

Apple announced the HomePod today! Yeah! Finally Apple has a simple multiuser interface to Siri so that all of those Homekit users can engage with their Smart Home investment without whipping out the app. Except it might be too late…

Apple HomePod Announced on 5 June 2017, with availability in Dec 2017, arrives three years after Amazon Echo is launched.

Clearly meant to compete with the Amazon Echo, the HomePod is a bit late on the scene, similar to Nokia’s late response to the threat of the iPhone, something I was on the front lines of. The difference here is that Apple is the one late to the party. Amazon’s dominance in this space will be a tough nut to crack, especially with their Community Garden approach to engagement. Even though Apple has opened up Siri to developers, the momentum behind Amazon in the home assistant category is eerily similar to Apple’s domination of the Smartphone market with the launch of the iPhone in January of 2007. When we look at the mentions of Alexa vs Siri across Smart Home Device and App reviews going back to January 2016, you can see the dominance enjoyed by Amazon (to be fair, we removed all reviews of Google Home and Amazon Alexa products (including Echo, Dot and Tap) from the dataset).

Amazon Alexa mentions dominate those of Siri within Smart Home Device and App Reviews

Homekit saw a blip on the scope when the new version was announced in September of 2016 but lost out nearly 8:1 to Amazon Alexa over the holiday season. Question is, will the HomePod shift this graph? Will we see consumers clamoring in Holiday 2017 for HomePod when pricing and performance are still unknown? Priced at $349, is it worth that much for Apple fans to display their existing high end bluetooth speakers with a multiuser voice controlled DJ? Time will tell…

Apple did nail one thing. HomePod is about music first and Smart Home engagement second. This echos, pardon the pun, what we are seeing about consumer use of Google Home and Amazon Echo. Music rules the Smart Home! Plus we are also seeing an increase in demand for smart switches, sensors and lighting as these voice assistants make it easier to control these oft underlook elements of the Smart Home rather than whipping out yet another app to make sure the guest room light is off…

As with all Smart Home devices, we’ll be tracking the launch closely as December approaches. You can already see the impact of HomePod on the Smart Home narrative in our analysis of the social conversation around the market. Will Apple avoid Nokia’s fate and make a dent on Amazon’s substantial lead or will Apple become the disrupted? You can watch it unfold using the Argus Analyzer, our Real-Time IoT Ecosystem Intelligence tool!